{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ab871661-8f08-41d5-b761-8e91869ae2c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "葡萄酒数据集如下：\n",
      "     label     a1    a2    a3    a4   a5    a6    a7    a8    a9    a10   a11  \\\n",
      "0        1  14.23  1.71  2.43  15.6  127  2.80  3.06  0.28  2.29   5.64  1.04   \n",
      "1        1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28   4.38  1.05   \n",
      "2        1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81   5.68  1.03   \n",
      "3        1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18   7.80  0.86   \n",
      "4        1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82   4.32  1.04   \n",
      "..     ...    ...   ...   ...   ...  ...   ...   ...   ...   ...    ...   ...   \n",
      "173      3  13.71  5.65  2.45  20.5   95  1.68  0.61  0.52  1.06   7.70  0.64   \n",
      "174      3  13.40  3.91  2.48  23.0  102  1.80  0.75  0.43  1.41   7.30  0.70   \n",
      "175      3  13.27  4.28  2.26  20.0  120  1.59  0.69  0.43  1.35  10.20  0.59   \n",
      "176      3  13.17  2.59  2.37  20.0  120  1.65  0.68  0.53  1.46   9.30  0.60   \n",
      "177      3  14.13  4.10  2.74  24.5   96  2.05  0.76  0.56  1.35   9.20  0.61   \n",
      "\n",
      "      a12   a13  \n",
      "0    3.92  1065  \n",
      "1    3.40  1050  \n",
      "2    3.17  1185  \n",
      "3    3.45  1480  \n",
      "4    2.93   735  \n",
      "..    ...   ...  \n",
      "173  1.74   740  \n",
      "174  1.56   750  \n",
      "175  1.56   835  \n",
      "176  1.62   840  \n",
      "177  1.60   560  \n",
      "\n",
      "[178 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names=['label','a1','a2','a3','a4','a5','a6','a7','a8','a9','a10','a11','a12','a13']\n",
    "dataset=pd.read_csv(\"wine.data\",names=names)\n",
    "print(\"葡萄酒数据集如下：\")\n",
    "print(dataset)\n",
    "data=dataset.iloc[range(0,178),range(1,14)]\n",
    "target=dataset.iloc[range(0,178),range(0,1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "48923682-6542-4b42-9c94-9bb46bf66c2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a 1 中异常值: []\n",
      "a 2 中异常值: [5.8 5.51 5.65]\n",
      "a 3 中异常值: [1.36 3.22 3.23]\n",
      "a 4 中异常值: [10.6 30.0 28.5 28.5]\n",
      "a 5 中异常值: [151.0 139.0 136.0 162.0]\n",
      "a 6 中异常值: []\n",
      "a 7 中异常值: []\n",
      "a 8 中异常值: []\n",
      "a 9 中异常值: [3.28 3.58]\n",
      "a 10 中异常值: [10.8 13.0 11.75 10.68]\n",
      "a 11 中异常值: [1.71]\n",
      "a 12 中异常值: []\n",
      "a 13 中异常值: []\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#plt.style.use('seaborn-darkgrid')\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "data.plot(kind='box',subplots=True,layout=(3,5),sharex=False,sharey=False)\n",
    "p=data.boxplot(return_type='dict')\n",
    "for i in range(13):\n",
    "    y=p['fliers'][i].get_ydata()\n",
    "    print('a',i+1,'中异常值:',y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b46bb96d-16ea-4802-b5ee-18d45e0b3058",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.51861254 -0.56906261  0.26105088 ...  0.39346131  1.83381234\n",
      "   1.01300893]\n",
      " [ 0.24628963 -0.50234086 -0.90869274 ...  0.43875109  1.10735109\n",
      "   0.96524152]\n",
      " [ 0.19687903  0.05049647  1.22911457 ...  0.34817153  0.7860317\n",
      "   1.39514818]\n",
      " ...\n",
      " [ 0.33275817  1.88057869 -0.4246609  ... -1.64457872 -1.46320409\n",
      "   0.28057537]\n",
      " [ 0.20923168  0.26972507  0.01903496 ... -1.59928894 -1.37938164\n",
      "   0.29649784]\n",
      " [ 1.39508604  1.70900848  1.51146647 ... -1.55399916 -1.40732245\n",
      "  -0.59516041]]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "names=['label','a1','a2','a3','a4','a5','a6','a7','a8','a9','a10','a11','a12','a13']\n",
    "dataset=pd.read_csv(\"wine-clean.data\",names=names)\n",
    "data=dataset.iloc[range(0,178),range(1,14)]\n",
    "target=dataset.iloc[range(0,178),range(0,1)].values.reshape(1,178)[0]\n",
    "cdata=preprocessing.StandardScaler().fit_transform(data)\n",
    "print(cdata)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1d6132ca-5e8c-4636-88fa-ff3053f7fd03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "x,y=cdata,target\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=0)\n",
    "k_range=range(1,15)\n",
    "k_error=[]\n",
    "for k in k_range:\n",
    "    model=KNeighborsClassifier(n_neighbors=k)\n",
    "    scores=cross_val_score(model,x,y,cv=5,scoring='accuracy')\n",
    "    k_error.append(1-scores.mean())\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.plot(k_range,k_error,'r-')\n",
    "plt.xlabel('k的取值')\n",
    "plt.ylabel('预测误差值')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "032f6864-37ff-4969-88af-a822537caa17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型预测准确率: 0.9555555555555556\n",
      "测试集的预测标签: [1 3 2 1 2 1 1 3 2 2 3 3 1 2 3 2 1 1 3 1 2 1 1 2 2 2 2 2 2 3 1 1 2 1 1 1 3\n",
      " 2 2 3 1 1 2 2 2]\n",
      "测试集的真实标签: [1 3 2 1 2 2 1 3 2 2 3 3 1 2 3 2 1 1 2 1 2 1 1 2 2 2 2 2 2 3 1 1 2 1 1 1 3\n",
      " 2 2 3 1 1 2 2 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "model=KNeighborsClassifier(n_neighbors=9)\n",
    "model.fit(x_train,y_train)\n",
    "pred=model.predict(x_test)\n",
    "ac=accuracy_score(y_test,pred)\n",
    "print(\"模型预测准确率:\",ac)\n",
    "print(\"测试集的预测标签:\",pred)\n",
    "print(\"测试集的真实标签:\",y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0421a979-cc0a-49e8-8b25-77c46cee4087",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in d:\\programdata\\anaconda3\\lib\\site-packages (0.13.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in d:\\programdata\\anaconda3\\lib\\site-packages (from seaborn) (1.26.4)\n",
      "Requirement already satisfied: pandas>=1.2 in d:\\programdata\\anaconda3\\lib\\site-packages (from seaborn) (2.2.2)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in d:\\programdata\\anaconda3\\lib\\site-packages (from seaborn) (3.8.4)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in d:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in d:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in d:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.51.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in d:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in d:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.2)\n",
      "Requirement already satisfied: pillow>=8 in d:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in d:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in d:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in d:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in d:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=1.2->seaborn) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in d:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n"
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